INSERM U562, F-91191 Gif/Yvette, France.
Curr Biol. 2009 Oct 13;19(19):1608-15. doi: 10.1016/j.cub.2009.08.047. Epub 2009 Sep 24.
Neuropsychology and human functional neuroimaging have implicated human parietal cortex in numerical processing, and macaque electrophysiology has shown that intraparietal areas house neurons tuned to numerosity. Yet although the areas responding overall during numerical tasks have been well defined by neuroimaging, a direct demonstration of individual number coding by spatial patterns has thus far been elusive.
We used multivariate pattern recognition on high-resolution functional imaging data to decode the information content of fine-scale signals evoked by different individual numbers. Parietal activation patterns for individual numerosities could be accurately discriminated and generalized across changes in low-level stimulus parameters. Distinct patterns were evoked by symbolic and nonsymbolic number formats, and individual digits were less accurately decoded (albeit still with significant accuracy) than numbers of dots. Interestingly, the numerosity of dot sets could be predicted above chance from the brain activation patterns evoked by digits, but not vice versa. Finally, number-evoked patterns changed in a gradual fashion as a function of numerical distance for the nonsymbolic notation, compatible with some degree of orderly layout of individual number representations.
Our findings demonstrate partial format invariance of individual number codes that is compatible with more numerous but more broadly tuned populations for nonsymbolic than for symbolic numbers, as postulated by recent computational models. In more general terms, our results illustrate the potential of functional magnetic resonance imaging pattern recognition to understand the detailed format of representations within a single semantic category, and beyond sensory cortical areas for which columnar architectures are well established.
神经心理学和人类功能神经影像学表明，人类顶叶皮层参与了数字处理，而猕猴电生理学表明，顶内区域中存在对数量敏感的神经元。然而，尽管神经影像学已经明确了在进行数字任务时整体反应的区域，但迄今为止，还难以直接证明空间模式的个体数字编码。
我们使用高分辨率功能成像数据的多元模式识别来解码由不同个体数字引起的精细信号的信息含量。个体数字的顶叶激活模式可以被准确区分，并在低水平刺激参数变化时进行概括。符号和非符号数字格式都能引发不同的模式，并且个体数字的解码精度较低（尽管仍然具有显著的准确性），而点的数量则能更准确地解码。有趣的是，从数字引起的大脑激活模式可以预测点集的数量，而不是相反。最后，非符号表示的数字距离的函数会逐渐改变数量引起的模式，这与个体数字表示的某种有序布局是一致的。
我们的发现表明，个体数字代码具有部分格式不变性，与符号数字相比，非符号数字的个体数字表示具有更多但更广泛的调谐群体，这与最近的计算模型的假设是一致的。更一般地说，我们的结果说明了功能磁共振成像模式识别理解单一语义类别内部表示的详细格式的潜力，以及超越了柱形结构已经确立的感觉皮层区域。
